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Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

期刊

ADVANCES IN APPLIED ENERGY
卷 3, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.adapen.2021.100055

关键词

Fault detection and diagnostics; Sensor fault; Sensor selection; Sensor calibration; Sensor analysis

资金

  1. National Renewable Energy Laboratory
  2. U.S. Department of Energy (DOE) [DE-AC36-08GO28308]
  3. U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Building Technologies Office

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A comprehensive literature review of over 100 FDD-sensor-related papers revealed a focus on FDD algorithms rather than sensors, with less attention on sensor hardware topics compared to software topics. Sensor engineering aspects are often neglected, and important sensor topics such as cost-effectiveness and sensor schema/layout/location are under-studied in the current research landscape.
Sensors are the key information source for fault detection and diagnostics (FDD) in buildings. However, sensors are often not properly designed, installed, calibrated, located, and maintained, which negatively impacts FDD performance. Several sensor-related FDD topics have been widely studied, covering a wide range of fault types and applications. However, it is difficult to get a clear picture of the technical development of sensor-related topics in FDD. A systematic review of sensor topics is needed to summarize the existing research in a logical way, draw conclusions on the current development, and predict the future development of sensors in building FDD. To address this gap, we conducted a comprehensive literature review of more than 100 FDD-sensor-related papers. In this article, we subdivide the FDD tasks into building-level, system-level, and component-level FDD, and review sensor-related topics in each category. Our major conclusions are: (a) current data-driven FDD research focuses more on FDD algorithms than sensors, (b) sensor hardware research topics are less studied than sensor software topics, (c) very few papers focus on sensor engineering as an integral aspect of FDD development, and (d) some important sensor topics, such as sensor cost-effectiveness and sensor schema/layout/location, are not well studied. Finally, we discuss the need for a systematic framework of FDD sensors and models to integrate sensor design/selection, sensor data analysis/mining, feature selection, physics-based or data-driven algorithm development, sensor fault detection, sensor calibration, and sensor maintenance. Finally, expert interviews are conducted to validate the above findings and conclusions.

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